Unveiling the Mechanisms of Neurofeedback Training with Pioneering Real-time fMRI technology

COEX 

Real-time fMRI neurofeedback has shown promising results, yet its clinical efficacy remains a topic of debate due to factors such as the lack of control conditions in many studies, potential noise and placebo effects, and an incomplete understanding of its underlying mechanisms. A primary challenge is the susceptibility of the fMRI signal to various types of noise, especially physiological variations. In closed-loop feedback training, if participants, whether consciously or unconsciously, discern that they can influence feedback signals based on physiological state changes, the objective of NF training may transition from modulating neural activity to altering physiological states. To address this challenge, we introduced an advanced real-time fMRI processing system, RTPSpy. This system delivers comprehensive noise reduction, including physiological noise, ensuring precise feedback on brain activation states. In this session, I will outline the capabilities of RTPSpy, with a focus on its proficiency in counteracting physiological noise. I will showcase the influence of physiological noise on fMRI signals and the benefits of real-time processing in mitigating it. Furthermore, I will delve into our unique approach to simultaneous EEG-fMRI recording, aiming to elucidate the mechanisms of neurofeedback training. By integrating EEG and fMRI data, we can obtain a temporally resolved view that distinguishes brain activity during the initial response to feedback signals and subsequent self-regulatory phases. I will present our findings underscoring the pivotal role of the brain's reaction to feedback signals in the efficacy of neurofeedback training. In addition, I will present initial results illustrating how the combined EEG-fMRI analysis can distinctly differentiate between the brain's response to feedback signals and self-regulatory activations.